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分析评估以人为中心的 CPPS 工作站弹性的定量指标。

Analysis of quantitative metrics for assessing resilience of human-centered CPPS workstations.

机构信息

Free University of Bozen-Bolzano, Piazetta del Università 1, 39100, Bozen-Bolzano, Italy.

出版信息

Sci Rep. 2023 Feb 20;13(1):2914. doi: 10.1038/s41598-023-29735-1.

DOI:10.1038/s41598-023-29735-1
PMID:36807299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9941514/
Abstract

Manufacturing companies' preparedness level against external and internal disruptions is complex to assess due to a lack of widely recognized or standardized models. Resilience as the measure to characterize preparedness against disruptions is a concept with various numerical approaches, but still lacking in the industry standard. Therefore, the main contribution of the research is the comparison of existing resilience metrics and the selection of the practically usable quantitative metric that allows manufacturers to start assessing the resilience in digitally supported human-centered workstations more easily. An additional contribution is the detection and highlighting of disruptions that potentially influence manufacturing workstations the most. Using five weighted comparison criteria, the resilience metrics were pairwise compared based on multi-criteria decision-making Analytic Hierarchy Process analysis on a linear scale. The general probabilistic resilience assessment method Penalty of Change that received the highest score considers the probability of disruptions and related cost of potential changes as inputs for resilience calculation. Additionally, manufacturing-related disruptions were extracted from the literature and categorized for a better overview. The Frequency Effect Sizes of the extracted disruptions were calculated to point out the most influencing disruptions. Overall, resilience quantification in manufacturing requires further research to improve its accuracy while maintaining practical usability.

摘要

由于缺乏广泛认可或标准化的模型,评估制造企业应对外部和内部干扰的准备程度非常复杂。弹性作为衡量应对干扰的准备程度的指标是一个具有各种数值方法的概念,但在行业标准中仍然缺乏。因此,这项研究的主要贡献是比较现有的弹性指标,并选择实际可用的定量指标,使制造商能够更容易地开始评估数字化支持的以人为中心的工作站的弹性。另一个贡献是检测和突出可能对制造工作站影响最大的干扰。使用五个加权比较标准,基于线性尺度的多准则决策层次分析对弹性指标进行了两两比较。获得最高分的一般概率弹性评估方法变更惩罚考虑了干扰的概率和潜在变化的相关成本作为弹性计算的输入。此外,还从文献中提取了与制造相关的干扰,并进行了分类,以便更好地了解。计算了提取干扰的频率效应大小,以指出最具影响力的干扰。总的来说,制造业的弹性量化需要进一步的研究,以提高其准确性,同时保持实用性。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/234bdfc4425f/41598_2023_29735_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/98c81e59c5fe/41598_2023_29735_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/eecd20407331/41598_2023_29735_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/0118dbd2ae73/41598_2023_29735_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/7472acdd1199/41598_2023_29735_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0241/9941514/234bdfc4425f/41598_2023_29735_Fig8_HTML.jpg

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